Litterature review on control of municipal aeration systems

19 Pages·2011·0.36 MB·English

Control of aeration systems in activated sludge
processes – a review

L. Åmand
IVL Swedish Environmental Research Institute/Department of Information Technology, Uppsala
University. (E-mail: [email protected])

Abstract
This review attempts to summarise and categorise research performed within the field of control of
continuous aeration systems in municipal wastewater treatment plants over the last ten years. The
review covers research into various methods to both decide and track the dissolved oxygen set-
point but also the control of the total aerated volume. With respect to dissolved oxygen set-point
control and determination, the strategies used for control span from modifications and
developments of simple control methods that have been explored since the 1970's, to advanced
control such as model-based predictive and feedback controllers. Also, fuzzy logic control has
gained more interest in the control of continuous processes and is utilised both in the context of
deciding and tracking set-points, but also to control the total aerobic volume. The review is
supplemented by a discussion on what level of complexity is required for an aeration control
system together with a summary of comparisons between control strategies evaluated in full-scale,
pilot scale and in simulations.
Keywords: Activated sludge; aeration; control strategies; wastewater treatment

INTRODUCTION
Making dissolved oxygen (DO) transfer from gas to liquid phase is a very energy
intensive activity in the treatment plant, as well as crucial for the treatment results. In a
nitrifying activated sludge plant, the DO level in an activated sludge process must satisfy
the microorganisms’ requirements to achieve near complete nitrification of influent
ammonia and aeration must also preserve a sufficient level of stirring in the basin to
avoid spontaneous settling and filamentous growth. A too high level of aeration is
unwanted since it is costly and creates little or no extra treatment effect. To meet the
above specifications is an important challenge for the control of aeration systems in
treatment plants.

The standard control handle for aeration control is the air flow rate to the aerobic basins
which will change the rate of oxygen transfer and hence the dissolved oxygen
concentration. This corresponds to the first level of aeration control. Second level control
represents the control of dissolved oxygen concentration and higher levels represent the
control of treatment results, most often expressed by the effluent ammonia concentration.
Apart from control of the dissolved oxygen concentration, other topics of interest for
aeration control are the dissolved oxygen profile along a plug-flow basin and control of
the aerobic reactor volume.

Overall goals for wastewater treatment on the legislative level involve effluent quality
limits of various kinds depending on country, size and effluent recipient. Given the
development of advanced treatment methods of late, wastewater can be treated to high
1
levels, given unlimited resources. However, it is not environmentally sound to treat too
much, given the resource consumption treatment requires. Therefore, an assumption of
cost-effectiveness and efficiency is often incorporated into the goal formulation, and
automatic process control is an important tool for achieving this.

The control goal for a plant is often specified in a similar manner to ―satisfy effluent
requirements consistently and minimise costs while maintaining water quality‖ (Olsson &
Jeppsson, 1994). Another example is ―maintain reliable effluent quality despite
disturbances against reasonable costs‖ (Weijers, 2000). A detailed goal is described in
Brdys et al. (2008): ―(i) to meet effluent discharge requirements, (ii) to keep biological
sustainability of activated sludge in the treatment plant, (iii) to minimise operational costs
(mostly cost of energy and chemicals), (iv) to minimise load of untreated sewage
discharged to the receiving waters‖.

Different aspects of ICA (instrumentation, control and automation) within the wastewater
industry and the water industry can be found in Olsson et al. (2005) and Olsson (2006),
respectively. Here, the control of unit processes including the aeration and DO control are
included and the first reference gives some examples of full-scale results. Weijers (2000)
presents a detailed list of control laws for wastewater treatment control up to then,
including aeration control. Another overview of different control systems is found in
Vanrolleghem (2007), even though the main goal of the report is to describe how model
simulation can be a tool in wastewater treatment operation. Jeppsson et al. (2002) provide
an overview of ICA from a European perspective and conclude that PI control or
variations thereof are the most common strategies in full-scale.

This review take a deeper look into aeration control of municipal activated sludge
systems with continuous aeration for the purpose of biological nitrogen removal.
Alternating or intermittent aeration systems, Sequencing Batch Reactors (SBR) and
industrial applications are only mentioned briefly when applicable. Also, applications on
conventional activated sludge are stressed, such as predenitrification and similar
configurations. Research into other control categories not addressing aeration control
explicitly, such as plant-wide control or control of precipitation of chemicals, is not
included. The review does not claim to be exhaustive and is focused on published
research during the last ten years. After describing the Benchmark Simulation Model, the
first part of the review covers oxygen set-point determination and tracking through
classical as well as more advanced methods. Special sections are devoted to control of the
aerobic volume and results from PhD theses during the period. Finally, the question of
what control method is the best is addressed and comparative evaluations between
methods in full-scale, pilot-scale and simulations are highlighted.
MODELLING AS AN EVALUATION TOOL
Many of the studies performed on aeration control and on other unit processes in
municipal wastewater treatment plants (WWTPs) are investigated through modelling and
simulation of the processes. A popular tool for this is the IWA/COST Simulation
Benchmark (BSM1) (Copp, 2002), in this review referred to as the Benchmark
Simulation Model. The Benchmark Simulation Model is an implementation of the
2
Activated Sludge Model No. 1 (ASM1) (Henze et al., 1987). In research on control and
modelling of WWTPs, ASM1 or simplifications of ASM1 are widely used for evaluation
purposes. In fact, full scale testing of control strategies is not very common in literature.
In the text that follows, referenced articles are basing results on simulations unless
otherwise stated. The positive thing about using benchmark simulations in the evaluation
of control strategies is the possibility to have a similar approach in the evaluation of
divergent control schemes. A review of how simulations can be used to improve
wastewater treatment operation is found in Vanrolleghem (2007). Jeppsson &
Vanrolleghem (2011) present publications related to the Benchmark Simulation Model.
DETERMINATION AND TRACKING OF THE DISSOLVED OXYGEN SET-
POINT
Classical control and modifications thereof
Classical control procedures, including PID and cascade control, are well known methods
for DO set-point tracking. Feedforward of influent ammonia to handle disturbance
rejection, often in combination with cascade control, has also been successfully
investigated and compared to other control strategies over the last years, see for instance
Ingildsen et al. (2002), Krause et al. (2002), Vrecko et al. (2003), Meyer & Pöpel (2003),
Yong et al. (2005), Vrecko et al. (2006), Zhang et al. (2008) and Thornton et al. (2010).
These studies include different types of feedforward controllers and model the
feedforward term differently, both in a linear and non-linear manner. Apart from
Ingildsen et al. (2002) and Thornton et al., 2010, all the previously mentioned
publications include simulation work and in some cases pilot plant testing.

A combined feedback controller with feedforward of influent ammonia is used to control
both the oxygen set-point and the number of aerated tanks in Krause et al. (2002). The
combined controller was better at suppressing ammonia peaks than using only feedback
control. The same conclusions were made by Vrecko et al. (2006) where the air flow was
reduced by 45% with a combined feedforward and ammonia cascade control method,
compared to PI control with a constant DO set-point. Using only cascade ammonia
control reduced the air flow by 23%. The results were verified in a pilot-plant.

An example of a full scale evaluation of a modified cascade controller at Käppala WWTP
in Sweden can be found in Thunberg et al. (2009). The goal here was to distribute the
oxygen demand more evenly along the process. Similar thoughts were presented in
(Sahlmann et al., 2002) where four different DO set-point combinations were compared
for three aerobic zones in an A2/O (anaerobi-anoxic-oxic) process. The zone distribution
of air flow and standardised oxygen transfer efficiency (αSOTE, measured by off gas
method) were analysed for different loads. Energy was saved by balancing the oxygen
demand and minimising unnecessary sludge stabilisation.

Ingildsen et al. (2002b) present a methodology to balance cost, quality and robustness in
the selection of set-points for PI control of DO and internal nitrate recycle. Benedetti et
al. (2010) use cost functions for evaluation of different control strategies in combination
with Monte Carlo simulations and conclude that ammonia cascade PI control offer
greater environmental and economic benefits than simple PI control.
3
Classical PID control has been investigated and developed further. As an example,
multivariable PID (MPID) control design has been investigated in Wahab et al. (2009)
where simulations are used to evaluate different decoupling methods for MPID tuning
which only require step or frequency response tests. Tzoneva (2007) evaluates two
standard PID tuning methods (Ziegler-Nichols and a relay tuning method) using the
Benchmark Simulation Model and also demonstrates how to perform real time tuning.
Yoo & Kim (2009) performed full scale testing where different autotuning methods for
PID control of DO set-points are evaluated in an industrial WWTP. Together with
estimation of the oxygen transfer function (K a) and respiration rate (R) proposed by
L
Lindberg (1997) and a set-point decision law based on the estimated R, the study
demonstrated more stable treatment results for COD (chemical oxygen demand) and an
energy saving potential. A similar approach is presented in Yoo et al. (2002). The DO
set-point is also controlled in a study by Suescun et al. (2001) which develop ideas
presented in Lindberg & Carlsson (1996). The set-point is adjusted every four hours to
compensate for the deviation in actual effluent ammonia compared to the set-point. The
DO is controlled with conventional control. There are facultative zones which can be
aerobic/anoxic depending on operational aspects. A second SISO (single input single
output) controller controls the nitrate by adjusting the internal nitrate recycle flow rate.
The controller is verified with good results in a pilot plant. The control loops for nitrate
and ammonia were eventually combined with a similar control loop for suspended solids
and successfully verified in full scale at the Galindo-Bilbao WWTP (Ayesa et al., 2006).
NH4,in
FF
DOSP PI Process DO NH4,SP+ PI + +DOSP+ PI Qair,SP+ – PI Process NQHair4
+ - – – DO
(a) (d)
Membership
functions Rules
DOSP+– PI Qair,SP+ – PI Process QDaOir DOSP +– coFnutrzozlyle r Qair,SP + – PI Process DQOair
(b) (e)
Cost
Constraints function
Qair DOSP
NH4,SP +– PI DOSP+– PI Qair,SP+ – PI Process NDHO4 MPC Process DO
(c) (f)
Figure 1. Common control strategies: (a) DO feedback control with fixed set-point, (b) DO cascade control
with fixed DO set-point, (c) ammonia cascade control with fixed ammonia set-point, (d) ammonia cascade
control with ammonia feedforward, (e) Fuzzy DO control with fixed DO set-point and (f) MPC control of
air flow.

Rule-based control
In Krause et al. (2002) feedforward-feedback control to determine the DO set-point is
combined with rule-based control. Switching points in the operation determine whether
the set-points should be increased or decreased in steps. An earlier example of this in a
full-scale step-feed plant is found in Husmann et al. (1998).
4
A rule-based feedforward methodology was developed by Shen et al. (2010) for an A2/O
process, based on the cumulative frequency distribution of the influent ammonia and the
C/N ratio. Optimal set-points for the feedforward rules was created based on steady state
simulations and validated in dynamic simulations. No feedback was used.

Rule-based control in the form of fuzzy logic control was traditionally applied for
alternating systems and batch reactors; see for instance Traoré et al. (2005), Fiter et al.,
(2005). The trend has recently been to expand the application towards continuous
operation in both simulation studies and in pilot and full scale evaluations. Fuzzy logic
control is appreciated for its transparency and the possibility to include expert process
knowledge into the control system development.

Meyer & Pöpel (2003) performed simulations and pilot plant testing of a
predenitrification system controlling the DO set-point and the ratio of aerobic and anoxic
zones using a fuzzy controller. The system combined feedforward of influent ammonia
with feedback effluent ammonia and nitrate as well as the effluent ammonia time
variation. Compared to a fixed set-point of DO with relay control (alternating between 0
and 2 mg/l) and constant zone division, the fuzzy controller air flow was decreased by
24%.

A similar approach to control was made by Yong et al. (2006), where apart from the DO
set-points, the external carbon dosage was also controlled. Influent and effluent ammonia
was used as inputs to the controller and good performance was experienced in
simulations and in pilot scale testing. A similar strategy was found in Serralta et al.
(2002) where DO and nitrate is controlled with supervisory and fuzzy control.

A full scale implementation of a fuzzy logic system in a predenitrification system is
found in Baroni et al. (2006). Here, both the DO set-point and the air supply was
controlled through fuzzy logics. The installations were running for approximately a year
and produced long term and short term process stability as well as energy savings. A
sophisticated version of fuzzy control was presented by Han et al. (2008). The simulation
study employ a piecewise linearised relationship between the air flow rate and DO. PI
parameter values are tuned for each linear section. A blending of the PI controllers is
performed based on Gaussian membership functions.

Evaluation criteria and cost functions
To determine optimality, performance indices have been used extensively for evaluation.
In the 1990’s there was a lot of development of evaluation criteria and cost functions for
wastewater treatment modelling and control. Vanrolleghem et al. (1996) discussed the
implementation of an overall decision support index for design and operation of WWTPs
to balance cost and treatment results. The construction of such a parameter was further
elaborated upon in Vanrolleghem & Gillot (2002) where a total cost index (TCI) was
proposed that weighs different operating costs and investments. The same year, Copp et
al. (2002) presented performance indices for the evaluation of control strategies based on
simulations in the Benchmark Simulation Model. The indices include the Effluent
5
Quality Index (EQI) which integrates the total amount of pollutants for the process with
different weights depending on their severity. Another index is the Effluent Violations,
which is the percentage of time that the constraints are not met. There are also several
Operational Variables, including sludge production, pumping energy, aeration energy and
mixing energy. The method outlined in Copp et al. (2002) is commonly used when cost
functions or performance indices are applied in simulation studies.

Model-based control and hierarchical control
Model-based control includes a large group of control algorithms which all make use of a
process model of some kind in the control law. The model can be either black-box or
incorporate process equations similar to the ones in ASM1. Often the model can be used
to find a controller output that is optimal in some sense. Optimal control, as defined here,
assumes a cost function to be mathematically minimised and attempts to find the best
solution to the problem given constraints on the optimisation. Both feedback model-based
control, such as Linear Quadratic Control (LQC), and predictive control, such as Model
Predictive Control (MPC), minimise a cost function. MPC has become a popular control
method within many industries for its ability to handle constraints and to include multiple
variables. It has been a research topic for WWTPs since the mid 1990’s. There are many
developments of the classical MPC method, such as robust MPC, adaptive MPC and non-
linear MPC (Weijers, 2000).

Recent full-scale results from an MPC for aeration control is found in O'Brien et al.
(2011). Here, the previous on/off control strategy for the surface aerators in an activated
sludge process for BOD-removal is improved by using a black-box model of the aeration
in combination with feedforward of the influent BOD load. A Data Quality Monitor
improved the control by removing unreliable measurements.

The set-point of DO was determined with MPC in Sanchez & Katebi (2003). Sub-space
identification is used to create models for dissolved oxygen and the authors compare
three different MPC controllers with a single PI controller with constant set-point. The
evaluation is performed mainly through comparing system overshoot and settling time.
The authors conclude that a set-point controller improves the performance and find the
controller structure easy to implement.

DO tracking by MPC with a process model incorporating classical DO dynamics is
verified by Holenda et al. (2008). The effect of sampling time is investigated, but the
MPC controller compared to standard PI control only shows marginal improvements on
EQI.

Machado et al. (2009) decide the most economical set-point for a number of
decentralised controllers, controlling for instance the ammonium in the effluent of an
A2/O process. Flores-Alsina (2008) suggests a hierarchical multicriteria optimisation
method and applies it both to design of a WWTP and to decide set-points for DO and
effluent nitrate. The evaluation resulted in a very low (0.5 mg/L) set-point to promote
denitrification.

6
Steffens & Lant (1999) published results based on different model-based controllers in
comparison with classical PI control with fixed DO set-point and set-point through PI
ammonia control. The control handles were the DO set-point, internal recycle flow rate,
returned activated sludge flow rate and external carbon dosing. The model-based
controllers included were LQC, DMC (Dynamic Matrix Control) and non-linear
predictive control (NPC). The NPC controller outperformed the other controllers, and
improvements were experienced by both DMC and LQC compared to PI control. A later
comparison between different model-based control methods is found in Shen et al.
(2009). Here, DMC, Quadratic DMC (QDMC) and nonlinear MPC were investigated.
QDMC is not reported to outperform DMC, and the nonlinear MPC improves
performance but at the cost of increased energy consumption. In the same study,
feedforward for disturbance rejection is investigated in combination with DMC.
Ammonia concentration feedforward brings better results than inflow rate feedforward
and a combination of these is even better. Again, performance is improved through
feedforward but with increased energy consumption. A similar study (Shen et al. 2008)
published the year before compared QDMC, QDMC with feed-forward and nonlinear
MPC. The authors conclude that non-linear MPC handles disturbances best and with
acceptable energy consumption.

Zarrad et al. (2004) and Vilanova et al. (2009) compare decentralized PI regulators to
multivariable model-based controllers in the Benchmark Simulation Model. In the first
paper, a nitrate recycle PI loop and an air flow rate PI loop is compared to two model-
based controllers. One LQ controller where the disturbance is considered as noise and
one disturbance accommodation controller (DAC), which augments the process model
with a disturbance model. The PI controllers demonstrated better results than DAC but
the state estimator in the DAC was proposed as a tool for estimation of unmeasured
disturbances. Vilanova et al. (2009) compare the performance of a multiloop PI controller
to a multivariable controller in a single aerated reactor. DO and substrate concentration
are considered. The results when analysing step responses are comparable for the two
controllers.

Examples of where Genetic algorithms have been used for model-based control includes
Yamanaka et al. (2006) and Beraud et al. (2009). Yamanaka et al. (2006) evaluate a cost
minimisation control scheme using the Benchmark Simulation Model. The control
structure is hierarchical where a higher level controller provides the set-points of the
process based on optimisation using a simplified process model and Genetic algorithms.
The lower level controller follows the set-points. In the case of aeration control, the
optimiser determines an appropriate ammonia set-point which the lower level PI
controller follows. Sensitivity tests and validations of optimality are performed in the
Benchmark Simulation Model. Genetic algorithms were used by Beraud et al. (2009) to
optimize the set-point in three consecutive aerobic zones and obtain energy reductions of
10-20 % with maintained treatment performance.

A lot of research into model-based control in general and predictive control in particular
has been performed within the European research project SMAC (Smart Control of
Wastewater Systems). Several studies report on the oxygen concentration tracking. Brdys
7
& Konarzcak (2001) investigated a non-linear SISO MPC based on the oxygen dynamics.
The method was improved through increased computational efficiency by a fuzzy
predictive controller in Brdys & Diaz-Maiquez (2002). A nonlinear MPC and an
Adaptive Model Reference (DMRAC) controller were compared in Chotkowski et al.
(2005). In Piotrowski et al. (2008), the nonlinear MPC is supplemented by a model of the
blower system. The DO controllers above fit into the hierarchical control structure set up
by Brdys et al. (2008), which is divided into the supervisory control layer, optimising
control layer and follow-up control layer. The structure involves integrated control of a
treatment plant and sewer system and the optimising control layer involves slow, medium
and fast time scales. The manipulated control handle with respect to aeration control is
the DO concentration. The optimising control layer involves a MIMO robust MPC and
also other advanced methods (Extended Kalman filter, grey box parameter estimation,
Weighted Least Squares with Moving Measurement Window) and it is this layer that
produces the DO trajectory to the lower level DO controller in the follow-up control
layer. Three control strategies are proposed: normal control strategy, disturbed control
strategy and emergency control strategy. The objective of the process, and hence the
optimisation, varies depending on control strategy. In Brdys et al. (2008), the low-level
DO controller in the follow-up layer consists of a simple proportional controller,
however, the authors argue that a much better solution would be to apply an MPC, as
described above, for this purpose. The simulations in the report are based on real data
from the Katurzy UCT (University of Cape Town) treatment system in Poland.

Performance criteria and plant design
The ideas of performance criteria and optimality have been used extensively to propose
design methods for WWTPs. The design can be performed through mathematical
optimisation of a given cost function. Ayesa et al. (1998) and Rivas et al. (2008) optimise
the configuration of a step-feed BNR process, Doby et al. (2002) use Genetic algorithms
on a static WWTP model to propose design configurations and compare Genetic
algorithms to classical non-linear optimisation, Biswasa et al. (2007) optimize the
treatment train to suggest combinations of unit processes through Genetic algorithms,
Flores Alsina (2008) developed a conceptual design method based on optimisation for
design/redesign of WWTPs and Hakanen et al. (2008) use an interactive multiobjective
optimisation approach for design of plants. There are also examples where the cost
functions are used for evaluation and a systematic approach to design rather than optimal
design is applied, see for instance Benedetti et al. (2006) and Benedetti et al. (2008).
CONTROL OF AEROBIC VOLUME
Apart from changing the DO levels in the compartments the total aerobic volume can be
adjusted by turning aeration on or off in facultative zones. This is performed to
compensate for variations in incoming load and has often been performed by control
strategies based on estimations of the oxygen uptake rate (OUR), referred to as
respirometry. An early example can be found in Brouwer et al. (1998) who use a
feedforward model-based approach to determine the aerobic volume needed for complete
nitrification. Here, a simple process model together with estimation of biokinetic
parameters through batch respirometric measurements in one of the plant compartments
decides the aerobic volume. Evaluations are performed in a pilot plant. Baeza et al.
8
(2002) varied the total aerobic volume in a pilot plant fed with synthetic wastewater,
leading to a 10% increase in nitrogen removal compared to operation with a fixed
volume. Estimations of the COD were performed by turning off the aeration for short
periods, hence being able to calculate the OUR. The COD estimations served as inputs to
a neural network model which determined the total volume to be aerated. Similarly to
Brouwer et al. (1998), Samuelsson (2005) controls the ammonia concentration in the last
compartment through a model-based feedforward strategy which changes the aerobic
volume. The approach is based on on-line estimation of the reaction rate of ammonia and
combines feedforward with feedback control.

Svardal et al., 2003 use measured air flow to decide on how to adjust the aerobic volume
to the ammonia load. The method maximises the anoxic volume given the goal of
complete nitrification. The OUR is a good indicator of nitrification performance at low
ammonia concentrations, and is also proportional to the air flow rate. The method is
based on increasing the aerobic volume when the total air flow passes certain thresholds.
The paper presents full-scale results from the Lintz-Asten WWTP which uses an
oxidation ditch for nitrification and denitrification. The effluent ammonia concentrations
remained below 1 mg/L and the total nitrogen removal rate varied between 70% and
80%.

As mentioned above, Meyer & Pöpel (2003) use fuzzy logic to determine both the DO
set-point and the fraction of aerobic volume. Krause et al. (2002) also control both the
set-point and aerated volume. Through a feedforward model several switching points are
determined for compartments 3-5 in a predenitrification plant. Compartment five is
always aerated but can take on different DO set-points. Compartment 3 and 4 can be
either aerobic or anoxic with a preselected fixed set-point. Suescun et al. (2001) also
incorporate a facultative zone in the control which can be made aerobic if the DO set-
point is at its maximum level.

Ekman et al. (2006) developed a method for aeration volume control that only requires
measurements of the DO concentration. The method makes use of supervisory control
where two out of three zones can be either aerobic or anoxic depending on the DO
concentration in all three zones, creating a disturbance rejection effect. The strategy is
evaluated in the Benchmark Simulation Model and in the large pilot-plant facility
Hammarby Sjöstadsverk, treating municipal wastewater. Simulations compare the
strategy to constant DO set-point control and supervisory control based on ammonia
measurements.
DOCTORAL THESES
Weijers (2000) develops a thorough methodology for an improved control system design
approach. The thesis searches to construct a method for control goal formulation and
argues in support of a mathematical approach to set up the design problem. Controller
design is systematically covered including control structure design and control law
selection and the thesis includes a review of published control laws to date. The case
studies of the thesis where the proposed control system design approach is applied are
9
exclusively examples of model-based control. Both a predenitrification system and a
carousel system are evaluated by use of simulations with ASM1.

The thesis by Rosen (2001) describes challenges involved in monitoring and control of
wastewater treatment operation and outlines the possibilities for multivariate monitoring
and control. With respect to control of aeration systems, the thesis covers set-point
adjustments based on clustering to make the process return to its preferred process state
and a multivariate feedback controller that calculates appropriate set-points for the lower
level controllers. The techniques are applied to DO set-point control by simulations of a
reduced order ASM1.

Ingildsen (2002) presents valuable results on implementing control strategies in full-scale
operation and develops a method to guide the user through the implementation process of
new control structures in full-scale environments. In the full-scale experiments, the
author concludes that in-situ nutrient measurements in combination with simple control
strategies can improve the plant performance significantly. The practical trials have been
supported by simulations using the Benchmark Simulation Model. The assessed control
structure in relation to aeration control is supervisory feedback control of the effluent
ammonia combined with feedforward.

Different aspects of control of nitrogen removal are presented in Samuelsson (2005).
Several of those are related to denitrification and the addition of external carbon. The
work related to aeration control includes the control of the aerobic volume described
above.

Mulas (2006) applies the concept of self-optimising control (when acceptable
performance can be achieved with constant set-points of the controlled variables despite
disturbances) to aid in the selection of controlled variables. The analysis yields however
that the return and waste activated sludge rates are the two variables to control to adjust
the nitrate concentration and the sludge age, respectively.

Holenda (2007) investigates methods to optimise the aeration length in an alternating
system, but also develops an MPC controller for the purpose of DO set-point tracking.
The MPC is based on a linearised version of the ASM1.
ADVANCED OR SIMPLE CONTROL?
The control of aeration systems span from simple and straightforward PI and cascade
controllers, via fuzzy rule-based algorithms to model-based control and system
optimisation. A fair question to ask is: what control strategy is the best? Different types
of controllers can evidently handle different levels of aeration control, but the
recommendations from the scientific community in relation to the level of complexity of
the control system are not consistent. The scientific community agrees upon that the
activated sludge system in itself is a complex multidimensional and non-linear system
with several time scales involved and with interactions between process variables. But
the conclusions drawn from this fact diverge. Some argue that given the complex nature
of the system, simple control cannot guarantee the performance under a full range of
10